Not all mindfulness is equal: certain facets of mindfulness have important implications for well-being and mental health across the lifespan
Bibliographic record
Abstract
The connections between the five facets of mindfulness, well-being, and mental health across the lifespan have traditionally been investigated using variable-centered approaches. Less research has investigated these relationships from a person-centered, profile-based approach. In this work, we aimed to identify the profiles of mindfulness in a Canadian lifespan sample (14 to 90 years of age) and investigate how these profiles compared on age, well-being, and mental health. An age- and gender-balanced sample of 1,600 participants completed a questionnaire that measured the five facets of mindfulness; life satisfaction; existential well-being; and anxiety, depression, and stress symptoms. A latent profile analysis was conducted. Five profiles based on the five-facet model of mindfulness were identified: high mindfulness, moderate mindfulness, low mindfulness, nonjudgmentally aware, and judgmentally observing . The 3-step approach to profile comparisons was used to assess age, mental health, and well-being differences across the profiles. Those in the high mindfulness and nonjudgmentally aware profiles were generally older, while the judgmentally observing profile contained younger individuals. Those in the high mindfulness and nonjudgmentally aware profiles reported the greatest mental health and well-being. Conversely, those in the low mindfulness and judgmentally observing profiles had worse mental health than the other profiles. The moderate mindfulness profile was situated between these profile groups on age, mental health, and well-being outcomes. This pattern of results has implications for mindfulness-based intervention research and practice to better account for heterogeneity in mindfulness and better support well-being across the lifespan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".